Relational Graph Convolutional Networks for Sentiment Analysis (2404.13079v1)

Abstract: With the growth of textual data across online platforms, sentiment analysis has become crucial for extracting insights from user-generated content. While traditional approaches and deep learning models have shown promise, they cannot often capture complex relationships between entities. In this paper, we propose leveraging Relational Graph Convolutional Networks (RGCNs) for sentiment analysis, which offer interpretability and flexibility by capturing dependencies between data points represented as nodes in a graph. We demonstrate the effectiveness of our approach by using pre-trained LLMs such as BERT and RoBERTa with RGCN architecture on product reviews from Amazon and Digikala datasets and evaluating the results. Our experiments highlight the effectiveness of RGCNs in capturing relational information for sentiment analysis tasks.

Enhancing Sentiment Analysis with Relational Graph Convolutional Networks and Pre-trained LLMs

Introduction

Sentiment analysis serves a critical role in understanding public opinion across various digital platforms. Given the limitations of traditional machine learning models which often treat text as "bag-of-words" or rely purely on sequence processing, there is a growing interest in leveraging more sophisticated models that encapsulate richer contextual relationships within the data. This paper introduces the integration of Relational Graph Convolutional Networks (RGCNs) with pre-trained LLMs (BERT and RoBERTa) to bolster the sentiment analysis task, specifically focusing on interpreting relations in graph-structured data.

Methodology

The paper delineates a methodological framework that comprises pre-processing text data, constructing heterogeneous graphs, utilizing pre-trained LLMs to initialize node embeddings, and applying RGCNs for node classification within these graphs. The process begins with standard text pre-processing steps, advancing to a more complex operation of building heterogeneous graphs where nodes represent documents and words, and edges signify different relations such as co-occurrences and semantic similarities.

The integration of BERT and RoBERTa models serves to enhance initial node representations, thereby capturing deep semantic meanings before they are fed into the RGCN architecture. This setup is hypothesized to harness both the relational structure of the text and the rich feature extraction capabilities of pre-trained LLMs, thereby potentially outperforming conventional models that do not consider these relationships explicitly.

Evaluation

For the evaluation, experiments were conducted on two datasets: Amazon product reviews in English and customer reviews from Digikala, an online retailer in Iran, in Persian. The paper compared the performance of the proposed RGCN approach against baseline models which include BERT and RoBERTa without RGCNs. Evaluation metrics such as accuracy and F1-score were employed to quantify model performance across different setups, including both balanced and imbalanced data scenarios.

Results

The experiments demonstrate that the RGCN-based models outperformed traditional methods significantly, indicating the efficacy of integrating RGCNs with pre-trained LLMs in capturing the relational and contextual nuances of text data. Notably, the models that combined RoBERTa with RGCNs achieved the highest performance scores in comparison to other configurations, which includes BERT with and without RGCNs, as well as standalone BERT and RoBERTa models.

Implications and Future Directions

This research contributes to the growing body of knowledge on applying graph-based neural networks to natural language processing tasks. Theoretically, it affirms the potential of graph neural networks in handling complex data relationships, a crucial aspect often overlooked in traditional text processing pipelines.

Practically, the findings could influence future developments in AI systems for social media monitoring, customer feedback analysis, and other domains where sentiment analysis is pivotal. These systems could deliver more nuanced and context-aware insights into public sentiments, which are valuable for strategic decision-making.

Looking ahead, extending this work could involve exploring more dynamic and adaptive graph construction methods, incorporating multimodal data, or applying the approach to other NLP tasks such as intent detection or summarization. Additionally, addressing the scalability and computational efficiency of RGCN models can make them more practical for real-world applications where data volume and velocity are high.

In conclusion, this research highlights the advantages of a hybrid approach that combines the strengths of relational graph models and advanced LLMs, paving the way for more sophisticated and nuanced AI tools in the field of sentiment analysis. Future studies should continue to build on these findings, optimizing and innovating further to harness the full potential of graph-based neural networks in text analytics.